MIMO (Multiple Input and Multiple Output) is an essential technology to increase peak data rate or throughput for wireless communications. In a MIMO-based system, the transmitter has multiple antennas for transmitting information to a receiver. Each transmit antenna radiates energy representing the signal being transmitted. The receiver also has multiple antennas for receiving the transmitted signals. The signal received at each antenna has contributions from different ones of the transmit antennas. In a case of multi-layer transmission, where different transmission antenna transmit different information and in a case where there is no feedback information about the channel to the transmitter, the receiver attempts to separate out the different transmit antenna signal components.
A MIMO-based receiver separates out the signal received at the same antenna into respective individual signal streams transmitted from each transmit antenna. As for signal separation processing, MLD (Maximum Likelihood Detection), which could extract antenna diversity gain, gives the best performance. MLD calculates a metric based on squared Euclidean distance for every possible combination of modulation signals from received signals and selects the modulation signal combination which shows the maximum likelihood (minimum sum of the metric corresponds to the combination of modulation signal over streams). The complexity associated with MLD increases exponentially as a function of modulation order or number of transmit antenna, and thus becomes even more impractical for advanced wireless communication networks.
One conventional approach for separating out multiple antenna signal streams based on MLD involves combining QR-decomposition with the M-algorithm (QRM-MLD). QRM-MLD reduces the amount of signal processing compared to MLD while yielding similar performance. QR-decomposition involves decomposing a composite channel matrix associated with all signal propagation paths into a unitary matrix Q and an upper-triangular matrix R. The Hermitian matrix of Q is then calculated and multiplied with the matrix representing all received signal streams. This yields an upper-triangular matrix for the desired signal streams, plus noise. The bottom row of the resulting matrix includes only a particular signal stream without any interference from the other signal streams. This reduces the amount of signal processing resources needed to cancel inter-stream interference. Next, the M-algorithm selects a number of symbols according to the branch metric by M in metric calculation to compare at each stage corresponding to transmit antenna which further reduces the amount of metric calculations. However, QR-MLD still tends to be overly complex.
Another conventional approach employed in multi-antenna receivers is Successive Interference Cancellation (SIC). According to SIC, received signal streams are ranked, e.g., based on received power. The highest ranking stream is selected and a replica of that signal is regenerated based on symbols detected for that signal and the estimated channel response associated with the signal. The signal replica is then subtracted from the composite signal, cancelling the signal's influence on the composite signal. This way, symbols for the remaining signal streams can be detected absent interference from cancelled signal streams. The SIC process continues until all signal streams are detected. Detection performance improves with SIC as more signal streams are cancelled because the remaining signal streams can be detected with less interference. However, the performance of SIC is typically worse than MLD because antenna diversity gain is not expected for the first signal stream and only 2nd order diversity gain is expected for the second signal stream and so on. Generally, when the number of Tx antenna is T and the number of Rx antenna is R, ideal diversity order expected at n-th stream signal detection using SIC is (R−T+n) because it includes the signal of stream by (T−n+1) among R antennas at n-th signal stream detection. This may result in improper signal stream detection which in turn causes an erroneous signal replica to be cancelled, increasing interference for the remaining signal streams.
Yet another conventional interference cancellation approach involves combining iterative soft cancellation with parallel-type interference cancellation, e.g., as disclosed in the Watanabe et al. IEICE article entitled “A Study of MIMO System with QR Decomposition and Soft Interference Canceller”, IEICE Technical Report, RCS2005-112, pp. 31-36, November 2005. This approach uses iterative parallel interference cancellation using soft values which could get receiver diversity gain and suppress error propagation. QR decomposition gives fairly good performance in the initial estimation and cancellation resulting in quick convergence in iterative parallel soft interference cancellation. Then, this approach yields results similar to MLD, but with reduced processing. However, complexity is still impractical as well as QRM-MLD and it requires many turbo decoder iterations increasing the time needed to perform signal detection.